Directional features such as Skeleton Hinge Distribution are not computationally expensive, are fast, easy to explain and very efficient in identifying the writer of a handwritten text. Hinge Distribution techniques are responsible for several works that have been researched in the literature recently. In this work, an attempt is made to evaluate the importance of three factors, the skeleton information, information regarding the size of the text and information regarding the grey-scale intensity of the text, that might affect writer identification accuracy in applications that utilize Directional features. Towards that goal, four new Hinge Distribution features are suggested. More specifically, the Run Length Directional Hinge Distribution (RLDHD) considers all the available pixel information in the text and the Run Length Skeleton Directional Hinge Distribution (RLSDHD), a variation of the former that utilizes the skeleton